Fair re-ranking is equivalent to gradient descent on a ranking manifold under Walrasian equilibrium in an attention market, yielding the ManifoldRank algorithm that adjusts gradients for supply-side fairness costs and demand-side score predictions.
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Data portability scenarios in algorithmic pluralism produce varying effects on user utility across different recommendation algorithms.
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The Attention Market: Interpreting Online Fair Re-ranking as Manifold Optimization under Walrasian Equilibrium
Fair re-ranking is equivalent to gradient descent on a ranking manifold under Walrasian equilibrium in an attention market, yielding the ManifoldRank algorithm that adjusts gradients for supply-side fairness costs and demand-side score predictions.
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Multistakeholder Impacts of Profile Portability in a Recommender Ecosystem
Data portability scenarios in algorithmic pluralism produce varying effects on user utility across different recommendation algorithms.